Big data is a huge buzzword in the tech world. However, it’s not limited to it. Data analysis keeps spreading its influence through more and more industries. Suddenly, everyone realized that properly interpreted information is an extremely valuable business asset. That’s why advanced analytics projects began to boom recently. Hunting for data specialists becomes fiercer. Data market investments in 2018 were estimated to reach $9 billion. So, data science market is rapidly growing, presenting opportunities for start-ups to enter the niche.
For someone new to the industry, it may seem like companies are spending days and nights doing some heavy data analytics. And, of course, are uncovering unbelievable superhuman solutions. However, despite all the hype around supersonic power of data, 85% of data analytics projects fail. Wow, right?
Here’s the untold truth about big data obsession: barely anyone knows how to build and use such projects in the right way. Often companies become fooled with giant analytics projects like Amazon and Netflix. However, succeeding in advanced analytics project is rather exclusive than average, and requires high-end efforts. The recent study by NewVantage Survey stated that 77% of companies report having serious difficulties with adopting data analytics and AI technology.
Surprisingly, big data projects rarely fail due to lack of budget. In fact, companies often get lost in details and technical aspects. They lose sight of the big picture, which turns out to be an indispensable part of data analytics projects.
Let’s take a look at the most common reasons why your data analytics project could fail:
You don’t have the main goal
Imagine you’ve spent a hilarious amount of time, money and resources to build up a Titanic. And then, it just aimlessly wanders around the waters with absolutely no direction. Dumb feeling, right? Same thing with a data-driven project. While setting a purpose may seem evident, many companies either neglect it at the very start or slowly steer away from it in the process.
Analytics projects are extremely complex by nature. It involves multiple stages of the investigation, transformation, interpretation, and visualization of data. Besides, all of it has to be put into a sustainable framework and result in valuable solutions for your customers. Shortly, it’s difficult.
That’s why companies often make a huge mistake sinking in operational and technical formations. Actually, losing sight of the final goal is the main reason why most data analytics start-ups cripple. Make sure to set a rigid strategy before you hire technicians and invest in expensive research tools.
Your data lacks quality
Obviously, there are different sorts of data. Everything seems to be extremely oversaturated with data. However, in the world of information, you have to know how to separate relevant information from the average rubbish. Finding a jewel in the dunes of sand requires dedication and knowledge.
Of course, it wouldn’t be a real world if we all have done everything right. That’s why some companies succeed, and others fail. To provide genuinely useful, insightful data, you have to dive deep. Put some extra effort into getting lean and valuable information, and in combination with the right analysis it will pay off.
You’re not trying to solve your customers’ problems
Let’s get back to the business part again. Put yourself on your customer’s place. What do you expect from a company that provides data analytics for you? Definitely not vague and uncertain conclusions. When companies invest in big data, they want to make sure they not only get insights but also valuable solutions to resolve their problems.
Make sure to clarify worthy, helpful resolutions for your customers. To accomplish this, speak out all the details and expectations of your partnership to deliver the best experience.
You neglect the overall project strategy
Data analytics projects require heavy tech support and constant maintenance. No wonder it’s crucial to have skillful, experienced developers, and focus on technical solutions. Especially considering how costly and devastating errors can be in advanced analytics projects.
The case with the UK National Health Service project is probably the largest and the most expensive data project failure. The attempt of putting all patients records into a centralized system miserably broke down, flushing $15 billion. So, data projects errors cost a very high price.
Of course, data startups are well-aware of the potential risks and try to avoid it at any causes. That’s why companies overconcentrate on the ‘behind-the-scenes’ part while neglecting the big picture. Google and Netflix primarily succeed because of the formed vision and strategy, and not just the financial part.
Undoubtfully, the progress of a data analytics start-up hardly depends on managing tech operations at the highest level. However, ignoring a wholesome strategy, business objectives and customer-driven practices are likely dooming you for failure. Strive to balance both business and operational aspects, stick to a solid strategy, and rock!